[1] “T_Blind_fast_LM_FALSE” T_Blind_fast_LM_FALSE_0.05 [1]
“T_Blind_fast_LM_FALSE” T_Blind_fast_LM_FALSE_0.2 [1]
“T_Blind_fast_LM_FALSE” T_Blind_fast_LM_FALSE_0.4 [1]
“T_Blind_fast_LM_FALSE” T_Blind_fast_LM_FALSE_0.6 [1]
“T_Blind_fast_LM_FALSE” T_Blind_fast_LM_FALSE_0.8 [1]
“T_Blind_fast_LM_FALSE” T_Blind_fast_LM_FALSE_0.95
[1]
“T_Blind_fast_LM_TRUE” T_Blind_fast_LM_TRUE_0.05 [1]
“T_Blind_fast_LM_TRUE” T_Blind_fast_LM_TRUE_0.2 [1]
“T_Blind_fast_LM_TRUE” T_Blind_fast_LM_TRUE_0.4 [1]
“T_Blind_fast_LM_TRUE” T_Blind_fast_LM_TRUE_0.6 [1]
“T_Blind_fast_LM_TRUE” T_Blind_fast_LM_TRUE_0.8 [1]
“T_Blind_fast_LM_TRUE” T_Blind_fast_LM_TRUE_0.95
[1]
“T_Blind_fast_RLM_FALSE” T_Blind_fast_RLM_FALSE_0.05 [1]
“T_Blind_fast_RLM_FALSE” T_Blind_fast_RLM_FALSE_0.2 [1]
“T_Blind_fast_RLM_FALSE” T_Blind_fast_RLM_FALSE_0.4 [1]
“T_Blind_fast_RLM_FALSE” T_Blind_fast_RLM_FALSE_0.6 [1]
“T_Blind_fast_RLM_FALSE” T_Blind_fast_RLM_FALSE_0.8 [1]
“T_Blind_fast_RLM_FALSE” T_Blind_fast_RLM_FALSE_0.95
[1]
“T_Blind_fast_RLM_TRUE” T_Blind_fast_RLM_TRUE_0.05 [1]
“T_Blind_fast_RLM_TRUE” T_Blind_fast_RLM_TRUE_0.2 [1]
“T_Blind_fast_RLM_TRUE” T_Blind_fast_RLM_TRUE_0.4 [1]
“T_Blind_fast_RLM_TRUE” T_Blind_fast_RLM_TRUE_0.6 [1]
“T_Blind_fast_RLM_TRUE” T_Blind_fast_RLM_TRUE_0.8 [1]
“T_Blind_fast_RLM_TRUE” T_Blind_fast_RLM_TRUE_0.95
[1]
“T_Blind_pearson_LM_FALSE” T_Blind_pearson_LM_FALSE_0.05 [1]
“T_Blind_pearson_LM_FALSE” T_Blind_pearson_LM_FALSE_0.2 [1]
“T_Blind_pearson_LM_FALSE” T_Blind_pearson_LM_FALSE_0.4 [1]
“T_Blind_pearson_LM_FALSE” T_Blind_pearson_LM_FALSE_0.6 [1]
“T_Blind_pearson_LM_FALSE” T_Blind_pearson_LM_FALSE_0.8 [1]
“T_Blind_pearson_LM_FALSE” T_Blind_pearson_LM_FALSE_0.95
[1]
“T_Blind_pearson_LM_TRUE” T_Blind_pearson_LM_TRUE_0.05 [1]
“T_Blind_pearson_LM_TRUE” T_Blind_pearson_LM_TRUE_0.2 [1]
“T_Blind_pearson_LM_TRUE” T_Blind_pearson_LM_TRUE_0.4 [1]
“T_Blind_pearson_LM_TRUE” T_Blind_pearson_LM_TRUE_0.6 [1]
“T_Blind_pearson_LM_TRUE” T_Blind_pearson_LM_TRUE_0.8 [1]
“T_Blind_pearson_LM_TRUE” T_Blind_pearson_LM_TRUE_0.95
[1]
“T_Blind_pearson_RLM_FALSE” T_Blind_pearson_RLM_FALSE_0.05 [1]
“T_Blind_pearson_RLM_FALSE” T_Blind_pearson_RLM_FALSE_0.2 [1]
“T_Blind_pearson_RLM_FALSE” T_Blind_pearson_RLM_FALSE_0.4 [1]
“T_Blind_pearson_RLM_FALSE” T_Blind_pearson_RLM_FALSE_0.6 [1]
“T_Blind_pearson_RLM_FALSE” T_Blind_pearson_RLM_FALSE_0.8 [1]
“T_Blind_pearson_RLM_FALSE” T_Blind_pearson_RLM_FALSE_0.95
[1]
“T_Blind_pearson_RLM_TRUE” T_Blind_pearson_RLM_TRUE_0.05 [1]
“T_Blind_pearson_RLM_TRUE” T_Blind_pearson_RLM_TRUE_0.2 [1]
“T_Blind_pearson_RLM_TRUE” T_Blind_pearson_RLM_TRUE_0.4 [1]
“T_Blind_pearson_RLM_TRUE” T_Blind_pearson_RLM_TRUE_0.6 [1]
“T_Blind_pearson_RLM_TRUE” T_Blind_pearson_RLM_TRUE_0.8 [1]
“T_Blind_pearson_RLM_TRUE” T_Blind_pearson_RLM_TRUE_0.95
[1]
“T_Blind_spearman_LM_FALSE” T_Blind_spearman_LM_FALSE_0.05 [1]
“T_Blind_spearman_LM_FALSE” T_Blind_spearman_LM_FALSE_0.2 [1]
“T_Blind_spearman_LM_FALSE” T_Blind_spearman_LM_FALSE_0.4 [1]
“T_Blind_spearman_LM_FALSE” T_Blind_spearman_LM_FALSE_0.6 [1]
“T_Blind_spearman_LM_FALSE” T_Blind_spearman_LM_FALSE_0.8 [1]
“T_Blind_spearman_LM_FALSE” T_Blind_spearman_LM_FALSE_0.95
[1]
“T_Blind_spearman_LM_TRUE” T_Blind_spearman_LM_TRUE_0.05 [1]
“T_Blind_spearman_LM_TRUE” T_Blind_spearman_LM_TRUE_0.2 [1]
“T_Blind_spearman_LM_TRUE” T_Blind_spearman_LM_TRUE_0.4 [1]
“T_Blind_spearman_LM_TRUE” T_Blind_spearman_LM_TRUE_0.6 [1]
“T_Blind_spearman_LM_TRUE” T_Blind_spearman_LM_TRUE_0.8 [1]
“T_Blind_spearman_LM_TRUE” T_Blind_spearman_LM_TRUE_0.95
[1]
“T_Blind_spearman_RLM_FALSE” T_Blind_spearman_RLM_FALSE_0.05 [1]
“T_Blind_spearman_RLM_FALSE” T_Blind_spearman_RLM_FALSE_0.2 [1]
“T_Blind_spearman_RLM_FALSE” T_Blind_spearman_RLM_FALSE_0.4 [1]
“T_Blind_spearman_RLM_FALSE” T_Blind_spearman_RLM_FALSE_0.6 [1]
“T_Blind_spearman_RLM_FALSE” T_Blind_spearman_RLM_FALSE_0.8 [1]
“T_Blind_spearman_RLM_FALSE” T_Blind_spearman_RLM_FALSE_0.95
[1]
“T_Blind_spearman_RLM_TRUE” T_Blind_spearman_RLM_TRUE_0.05 [1]
“T_Blind_spearman_RLM_TRUE” T_Blind_spearman_RLM_TRUE_0.2 [1]
“T_Blind_spearman_RLM_TRUE” T_Blind_spearman_RLM_TRUE_0.4 [1]
“T_Blind_spearman_RLM_TRUE” T_Blind_spearman_RLM_TRUE_0.6 [1]
“T_Blind_spearman_RLM_TRUE” T_Blind_spearman_RLM_TRUE_0.8 [1]
“T_Blind_spearman_RLM_TRUE” T_Blind_spearman_RLM_TRUE_0.95
[1]
“T_Driven_fast_LM_FALSE” T_Driven_fast_LM_FALSE_0.05 [1]
“T_Driven_fast_LM_FALSE” T_Driven_fast_LM_FALSE_0.2 [1]
“T_Driven_fast_LM_FALSE” T_Driven_fast_LM_FALSE_0.4 [1]
“T_Driven_fast_LM_FALSE” T_Driven_fast_LM_FALSE_0.6 [1]
“T_Driven_fast_LM_FALSE” T_Driven_fast_LM_FALSE_0.8 [1]
“T_Driven_fast_LM_FALSE” T_Driven_fast_LM_FALSE_0.95
[1]
“T_Driven_fast_LM_TRUE” T_Driven_fast_LM_TRUE_0.05 [1]
“T_Driven_fast_LM_TRUE” T_Driven_fast_LM_TRUE_0.2 [1]
“T_Driven_fast_LM_TRUE” T_Driven_fast_LM_TRUE_0.4 [1]
“T_Driven_fast_LM_TRUE” T_Driven_fast_LM_TRUE_0.6 [1]
“T_Driven_fast_LM_TRUE” T_Driven_fast_LM_TRUE_0.8 [1]
“T_Driven_fast_LM_TRUE” T_Driven_fast_LM_TRUE_0.95
[1]
“T_Driven_fast_RLM_FALSE” T_Driven_fast_RLM_FALSE_0.05 [1]
“T_Driven_fast_RLM_FALSE” T_Driven_fast_RLM_FALSE_0.2 [1]
“T_Driven_fast_RLM_FALSE” T_Driven_fast_RLM_FALSE_0.4 [1]
“T_Driven_fast_RLM_FALSE” T_Driven_fast_RLM_FALSE_0.6 [1]
“T_Driven_fast_RLM_FALSE” T_Driven_fast_RLM_FALSE_0.8 [1]
“T_Driven_fast_RLM_FALSE” T_Driven_fast_RLM_FALSE_0.95
[1]
“T_Driven_fast_RLM_TRUE” T_Driven_fast_RLM_TRUE_0.05 [1]
“T_Driven_fast_RLM_TRUE” T_Driven_fast_RLM_TRUE_0.2 [1]
“T_Driven_fast_RLM_TRUE” T_Driven_fast_RLM_TRUE_0.4 [1]
“T_Driven_fast_RLM_TRUE” T_Driven_fast_RLM_TRUE_0.6 [1]
“T_Driven_fast_RLM_TRUE” T_Driven_fast_RLM_TRUE_0.8 [1]
“T_Driven_fast_RLM_TRUE” T_Driven_fast_RLM_TRUE_0.95
[1]
“T_Driven_pearson_LM_FALSE” T_Driven_pearson_LM_FALSE_0.05 [1]
“T_Driven_pearson_LM_FALSE” T_Driven_pearson_LM_FALSE_0.2 [1]
“T_Driven_pearson_LM_FALSE” T_Driven_pearson_LM_FALSE_0.4 [1]
“T_Driven_pearson_LM_FALSE” T_Driven_pearson_LM_FALSE_0.6 [1]
“T_Driven_pearson_LM_FALSE” T_Driven_pearson_LM_FALSE_0.8 [1]
“T_Driven_pearson_LM_FALSE” T_Driven_pearson_LM_FALSE_0.95
[1]
“T_Driven_pearson_LM_TRUE” T_Driven_pearson_LM_TRUE_0.05 [1]
“T_Driven_pearson_LM_TRUE” T_Driven_pearson_LM_TRUE_0.2 [1]
“T_Driven_pearson_LM_TRUE” T_Driven_pearson_LM_TRUE_0.4 [1]
“T_Driven_pearson_LM_TRUE” T_Driven_pearson_LM_TRUE_0.6 [1]
“T_Driven_pearson_LM_TRUE” T_Driven_pearson_LM_TRUE_0.8 [1]
“T_Driven_pearson_LM_TRUE” T_Driven_pearson_LM_TRUE_0.95
[1]
“T_Driven_pearson_RLM_FALSE” T_Driven_pearson_RLM_FALSE_0.05 [1]
“T_Driven_pearson_RLM_FALSE” T_Driven_pearson_RLM_FALSE_0.2 [1]
“T_Driven_pearson_RLM_FALSE” T_Driven_pearson_RLM_FALSE_0.4 [1]
“T_Driven_pearson_RLM_FALSE” T_Driven_pearson_RLM_FALSE_0.6 [1]
“T_Driven_pearson_RLM_FALSE” T_Driven_pearson_RLM_FALSE_0.8 [1]
“T_Driven_pearson_RLM_FALSE” T_Driven_pearson_RLM_FALSE_0.95
[1]
“T_Driven_pearson_RLM_TRUE” T_Driven_pearson_RLM_TRUE_0.05 [1]
“T_Driven_pearson_RLM_TRUE” T_Driven_pearson_RLM_TRUE_0.2 [1]
“T_Driven_pearson_RLM_TRUE” T_Driven_pearson_RLM_TRUE_0.4 [1]
“T_Driven_pearson_RLM_TRUE” T_Driven_pearson_RLM_TRUE_0.6 [1]
“T_Driven_pearson_RLM_TRUE” T_Driven_pearson_RLM_TRUE_0.8 [1]
“T_Driven_pearson_RLM_TRUE” T_Driven_pearson_RLM_TRUE_0.95
[1]
“T_Driven_spearman_LM_FALSE” T_Driven_spearman_LM_FALSE_0.05 [1]
“T_Driven_spearman_LM_FALSE” T_Driven_spearman_LM_FALSE_0.2 [1]
“T_Driven_spearman_LM_FALSE” T_Driven_spearman_LM_FALSE_0.4 [1]
“T_Driven_spearman_LM_FALSE” T_Driven_spearman_LM_FALSE_0.6 [1]
“T_Driven_spearman_LM_FALSE” T_Driven_spearman_LM_FALSE_0.8 [1]
“T_Driven_spearman_LM_FALSE” T_Driven_spearman_LM_FALSE_0.95
[1]
“T_Driven_spearman_LM_TRUE” T_Driven_spearman_LM_TRUE_0.05 [1]
“T_Driven_spearman_LM_TRUE” T_Driven_spearman_LM_TRUE_0.2 [1]
“T_Driven_spearman_LM_TRUE” T_Driven_spearman_LM_TRUE_0.4 [1]
“T_Driven_spearman_LM_TRUE” T_Driven_spearman_LM_TRUE_0.6 [1]
“T_Driven_spearman_LM_TRUE” T_Driven_spearman_LM_TRUE_0.8 [1]
“T_Driven_spearman_LM_TRUE” T_Driven_spearman_LM_TRUE_0.95
[1]
“T_Driven_spearman_RLM_FALSE” T_Driven_spearman_RLM_FALSE_0.05 [1]
“T_Driven_spearman_RLM_FALSE” T_Driven_spearman_RLM_FALSE_0.2 [1]
“T_Driven_spearman_RLM_FALSE” T_Driven_spearman_RLM_FALSE_0.4 [1]
“T_Driven_spearman_RLM_FALSE” T_Driven_spearman_RLM_FALSE_0.6 [1]
“T_Driven_spearman_RLM_FALSE” T_Driven_spearman_RLM_FALSE_0.8 [1]
“T_Driven_spearman_RLM_FALSE” T_Driven_spearman_RLM_FALSE_0.95
[1]
“T_Driven_spearman_RLM_TRUE” T_Driven_spearman_RLM_TRUE_0.05 [1]
“T_Driven_spearman_RLM_TRUE” T_Driven_spearman_RLM_TRUE_0.2 [1]
“T_Driven_spearman_RLM_TRUE” T_Driven_spearman_RLM_TRUE_0.4 [1]
“T_Driven_spearman_RLM_TRUE” T_Driven_spearman_RLM_TRUE_0.6 [1]
“T_Driven_spearman_RLM_TRUE” T_Driven_spearman_RLM_TRUE_0.8 [1]
“T_Driven_spearman_RLM_TRUE” T_Driven_spearman_RLM_TRUE_0.95

Printing the
analysis outputs
par(op)
par(mfrow=c(1,2),cex=0.6)
rownames(totBaM) <- thenames
rownames(totDeM) <- thenames
rownames(toUnmatM) <- thenames
rownames(unalteredM) <- thenames
rownames(Decorrleated_FractionM) <- thenames
rownames(Base_FractionM) <- thenames
rownames(Unaltered_FractionM) <- thenames
rownames(sparcityM) <- thenames
rownames(Average_Latent_SizeM) <- thenames
rownames(SigDeM) <- thenames
rownames(La_SignificantM) <- thenames
rownames(pbKNNaucM) <- thenames
rownames(pbKNNaccM) <- thenames
colnames(totBaM) <- thr
colnames(totDeM) <- thr
colnames(toUnmatM) <- thr
colnames(unalteredM) <- thr
colnames(Decorrleated_FractionM) <- thr
colnames(Base_FractionM) <- thr
colnames(Unaltered_FractionM) <- thr
colnames(sparcityM) <- thr
colnames(Average_Latent_SizeM) <- thr
colnames(SigDeM) <- thr
colnames(La_SignificantM) <- thr
colnames(pbKNNaucM) <- thr
colnames(pbKNNaccM) <- thr
pander::pander(totFe)
327
pander::pander(totBaM)
| T_Blind_fast_LM_FALSE |
4 |
48 |
65 |
69 |
23 |
4 |
| T_Blind_fast_LM_TRUE |
24 |
36 |
48 |
59 |
22 |
4 |
| T_Blind_fast_RLM_FALSE |
4 |
48 |
65 |
69 |
23 |
4 |
| T_Blind_fast_RLM_TRUE |
24 |
36 |
48 |
59 |
22 |
4 |
| T_Blind_pearson_LM_FALSE |
5 |
48 |
65 |
69 |
23 |
4 |
| T_Blind_pearson_LM_TRUE |
23 |
36 |
48 |
59 |
22 |
4 |
| T_Blind_pearson_RLM_FALSE |
50 |
53 |
64 |
69 |
23 |
4 |
| T_Blind_pearson_RLM_TRUE |
33 |
36 |
45 |
58 |
22 |
4 |
| T_Blind_spearman_LM_FALSE |
38 |
45 |
62 |
62 |
22 |
3 |
| T_Blind_spearman_LM_TRUE |
24 |
44 |
46 |
43 |
22 |
3 |
| T_Blind_spearman_RLM_FALSE |
52 |
59 |
60 |
61 |
23 |
3 |
| T_Blind_spearman_RLM_TRUE |
37 |
43 |
48 |
45 |
23 |
3 |
| T_Driven_fast_LM_FALSE |
61 |
47 |
66 |
70 |
23 |
4 |
| T_Driven_fast_LM_TRUE |
14 |
36 |
54 |
54 |
21 |
4 |
| T_Driven_fast_RLM_FALSE |
61 |
47 |
66 |
70 |
23 |
4 |
| T_Driven_fast_RLM_TRUE |
14 |
36 |
54 |
54 |
21 |
4 |
| T_Driven_pearson_LM_FALSE |
59 |
46 |
66 |
70 |
23 |
4 |
| T_Driven_pearson_LM_TRUE |
4 |
36 |
54 |
54 |
21 |
4 |
| T_Driven_pearson_RLM_FALSE |
64 |
51 |
62 |
72 |
23 |
4 |
| T_Driven_pearson_RLM_TRUE |
43 |
42 |
54 |
55 |
21 |
4 |
| T_Driven_spearman_LM_FALSE |
55 |
58 |
61 |
62 |
20 |
3 |
| T_Driven_spearman_LM_TRUE |
37 |
53 |
49 |
47 |
20 |
3 |
| T_Driven_spearman_RLM_FALSE |
55 |
54 |
61 |
63 |
21 |
3 |
| T_Driven_spearman_RLM_TRUE |
43 |
47 |
51 |
46 |
21 |
3 |
pander::pander(totDeM)
| T_Blind_fast_LM_FALSE |
320 |
241 |
180 |
124 |
30 |
4 |
| T_Blind_fast_LM_TRUE |
246 |
255 |
197 |
130 |
33 |
4 |
| T_Blind_fast_RLM_FALSE |
320 |
241 |
180 |
124 |
30 |
4 |
| T_Blind_fast_RLM_TRUE |
246 |
255 |
197 |
130 |
33 |
4 |
| T_Blind_pearson_LM_FALSE |
319 |
241 |
180 |
124 |
30 |
4 |
| T_Blind_pearson_LM_TRUE |
253 |
255 |
197 |
130 |
33 |
4 |
| T_Blind_pearson_RLM_FALSE |
216 |
211 |
179 |
124 |
30 |
4 |
| T_Blind_pearson_RLM_TRUE |
226 |
229 |
200 |
130 |
33 |
4 |
| T_Blind_spearman_LM_FALSE |
209 |
219 |
171 |
113 |
25 |
3 |
| T_Blind_spearman_LM_TRUE |
231 |
229 |
189 |
122 |
32 |
3 |
| T_Blind_spearman_RLM_FALSE |
191 |
197 |
175 |
113 |
25 |
3 |
| T_Blind_spearman_RLM_TRUE |
215 |
219 |
187 |
120 |
32 |
3 |
| T_Driven_fast_LM_FALSE |
196 |
240 |
177 |
119 |
32 |
4 |
| T_Driven_fast_LM_TRUE |
298 |
255 |
191 |
127 |
33 |
4 |
| T_Driven_fast_RLM_FALSE |
196 |
240 |
177 |
119 |
32 |
4 |
| T_Driven_fast_RLM_TRUE |
298 |
255 |
191 |
127 |
33 |
4 |
| T_Driven_pearson_LM_FALSE |
198 |
241 |
177 |
119 |
32 |
4 |
| T_Driven_pearson_LM_TRUE |
321 |
255 |
191 |
127 |
33 |
4 |
| T_Driven_pearson_RLM_FALSE |
188 |
214 |
183 |
116 |
32 |
4 |
| T_Driven_pearson_RLM_TRUE |
211 |
221 |
192 |
127 |
33 |
4 |
| T_Driven_spearman_LM_FALSE |
178 |
198 |
174 |
107 |
25 |
3 |
| T_Driven_spearman_LM_TRUE |
199 |
211 |
183 |
119 |
32 |
3 |
| T_Driven_spearman_RLM_FALSE |
189 |
201 |
175 |
107 |
25 |
3 |
| T_Driven_spearman_RLM_TRUE |
210 |
213 |
185 |
119 |
32 |
3 |
pander::pander(toUnmatM)
| T_Blind_fast_LM_FALSE |
4 |
48 |
65 |
69 |
23 |
4 |
| T_Blind_fast_LM_TRUE |
24 |
36 |
48 |
59 |
22 |
4 |
| T_Blind_fast_RLM_FALSE |
4 |
48 |
65 |
69 |
23 |
4 |
| T_Blind_fast_RLM_TRUE |
24 |
36 |
48 |
59 |
22 |
4 |
| T_Blind_pearson_LM_FALSE |
5 |
48 |
65 |
69 |
23 |
4 |
| T_Blind_pearson_LM_TRUE |
23 |
36 |
48 |
59 |
22 |
4 |
| T_Blind_pearson_RLM_FALSE |
50 |
53 |
64 |
69 |
23 |
4 |
| T_Blind_pearson_RLM_TRUE |
33 |
36 |
45 |
58 |
22 |
4 |
| T_Blind_spearman_LM_FALSE |
38 |
45 |
62 |
62 |
22 |
3 |
| T_Blind_spearman_LM_TRUE |
24 |
44 |
46 |
43 |
22 |
3 |
| T_Blind_spearman_RLM_FALSE |
52 |
59 |
60 |
61 |
23 |
3 |
| T_Blind_spearman_RLM_TRUE |
37 |
43 |
48 |
45 |
23 |
3 |
| T_Driven_fast_LM_FALSE |
61 |
47 |
66 |
70 |
23 |
4 |
| T_Driven_fast_LM_TRUE |
14 |
36 |
54 |
54 |
21 |
4 |
| T_Driven_fast_RLM_FALSE |
61 |
47 |
66 |
70 |
23 |
4 |
| T_Driven_fast_RLM_TRUE |
14 |
36 |
54 |
54 |
21 |
4 |
| T_Driven_pearson_LM_FALSE |
59 |
46 |
66 |
70 |
23 |
4 |
| T_Driven_pearson_LM_TRUE |
4 |
36 |
54 |
54 |
21 |
4 |
| T_Driven_pearson_RLM_FALSE |
64 |
51 |
62 |
72 |
23 |
4 |
| T_Driven_pearson_RLM_TRUE |
43 |
42 |
54 |
55 |
21 |
4 |
| T_Driven_spearman_LM_FALSE |
55 |
58 |
61 |
62 |
20 |
3 |
| T_Driven_spearman_LM_TRUE |
37 |
53 |
49 |
47 |
20 |
3 |
| T_Driven_spearman_RLM_FALSE |
55 |
54 |
61 |
63 |
21 |
3 |
| T_Driven_spearman_RLM_TRUE |
43 |
47 |
51 |
46 |
21 |
3 |
pander::pander(unalteredM)
| T_Blind_fast_LM_FALSE |
7 |
86 |
147 |
203 |
297 |
323 |
| T_Blind_fast_LM_TRUE |
81 |
72 |
130 |
197 |
294 |
323 |
| T_Blind_fast_RLM_FALSE |
7 |
86 |
147 |
203 |
297 |
323 |
| T_Blind_fast_RLM_TRUE |
81 |
72 |
130 |
197 |
294 |
323 |
| T_Blind_pearson_LM_FALSE |
8 |
86 |
147 |
203 |
297 |
323 |
| T_Blind_pearson_LM_TRUE |
74 |
72 |
130 |
197 |
294 |
323 |
| T_Blind_pearson_RLM_FALSE |
111 |
116 |
148 |
203 |
297 |
323 |
| T_Blind_pearson_RLM_TRUE |
101 |
98 |
127 |
197 |
294 |
323 |
| T_Blind_spearman_LM_FALSE |
118 |
108 |
156 |
214 |
302 |
324 |
| T_Blind_spearman_LM_TRUE |
96 |
98 |
138 |
205 |
295 |
324 |
| T_Blind_spearman_RLM_FALSE |
136 |
130 |
152 |
214 |
302 |
324 |
| T_Blind_spearman_RLM_TRUE |
112 |
108 |
140 |
207 |
295 |
324 |
| T_Driven_fast_LM_FALSE |
131 |
87 |
150 |
208 |
295 |
323 |
| T_Driven_fast_LM_TRUE |
29 |
72 |
136 |
200 |
294 |
323 |
| T_Driven_fast_RLM_FALSE |
131 |
87 |
150 |
208 |
295 |
323 |
| T_Driven_fast_RLM_TRUE |
29 |
72 |
136 |
200 |
294 |
323 |
| T_Driven_pearson_LM_FALSE |
129 |
86 |
150 |
208 |
295 |
323 |
| T_Driven_pearson_LM_TRUE |
6 |
72 |
136 |
200 |
294 |
323 |
| T_Driven_pearson_RLM_FALSE |
139 |
113 |
144 |
211 |
295 |
323 |
| T_Driven_pearson_RLM_TRUE |
116 |
106 |
135 |
200 |
294 |
323 |
| T_Driven_spearman_LM_FALSE |
149 |
129 |
153 |
220 |
302 |
324 |
| T_Driven_spearman_LM_TRUE |
128 |
116 |
144 |
208 |
295 |
324 |
| T_Driven_spearman_RLM_FALSE |
138 |
126 |
152 |
220 |
302 |
324 |
| T_Driven_spearman_RLM_TRUE |
117 |
114 |
142 |
208 |
295 |
324 |
pander::pander(Decorrleated_FractionM)
| T_Blind_fast_LM_FALSE |
0.979 |
0.737 |
0.550 |
0.379 |
0.0917 |
0.01223 |
| T_Blind_fast_LM_TRUE |
0.752 |
0.780 |
0.602 |
0.398 |
0.1009 |
0.01223 |
| T_Blind_fast_RLM_FALSE |
0.979 |
0.737 |
0.550 |
0.379 |
0.0917 |
0.01223 |
| T_Blind_fast_RLM_TRUE |
0.752 |
0.780 |
0.602 |
0.398 |
0.1009 |
0.01223 |
| T_Blind_pearson_LM_FALSE |
0.976 |
0.737 |
0.550 |
0.379 |
0.0917 |
0.01223 |
| T_Blind_pearson_LM_TRUE |
0.774 |
0.780 |
0.602 |
0.398 |
0.1009 |
0.01223 |
| T_Blind_pearson_RLM_FALSE |
0.661 |
0.645 |
0.547 |
0.379 |
0.0917 |
0.01223 |
| T_Blind_pearson_RLM_TRUE |
0.691 |
0.700 |
0.612 |
0.398 |
0.1009 |
0.01223 |
| T_Blind_spearman_LM_FALSE |
0.639 |
0.670 |
0.523 |
0.346 |
0.0765 |
0.00917 |
| T_Blind_spearman_LM_TRUE |
0.706 |
0.700 |
0.578 |
0.373 |
0.0979 |
0.00917 |
| T_Blind_spearman_RLM_FALSE |
0.584 |
0.602 |
0.535 |
0.346 |
0.0765 |
0.00917 |
| T_Blind_spearman_RLM_TRUE |
0.657 |
0.670 |
0.572 |
0.367 |
0.0979 |
0.00917 |
| T_Driven_fast_LM_FALSE |
0.599 |
0.734 |
0.541 |
0.364 |
0.0979 |
0.01223 |
| T_Driven_fast_LM_TRUE |
0.911 |
0.780 |
0.584 |
0.388 |
0.1009 |
0.01223 |
| T_Driven_fast_RLM_FALSE |
0.599 |
0.734 |
0.541 |
0.364 |
0.0979 |
0.01223 |
| T_Driven_fast_RLM_TRUE |
0.911 |
0.780 |
0.584 |
0.388 |
0.1009 |
0.01223 |
| T_Driven_pearson_LM_FALSE |
0.606 |
0.737 |
0.541 |
0.364 |
0.0979 |
0.01223 |
| T_Driven_pearson_LM_TRUE |
0.982 |
0.780 |
0.584 |
0.388 |
0.1009 |
0.01223 |
| T_Driven_pearson_RLM_FALSE |
0.575 |
0.654 |
0.560 |
0.355 |
0.0979 |
0.01223 |
| T_Driven_pearson_RLM_TRUE |
0.645 |
0.676 |
0.587 |
0.388 |
0.1009 |
0.01223 |
| T_Driven_spearman_LM_FALSE |
0.544 |
0.606 |
0.532 |
0.327 |
0.0765 |
0.00917 |
| T_Driven_spearman_LM_TRUE |
0.609 |
0.645 |
0.560 |
0.364 |
0.0979 |
0.00917 |
| T_Driven_spearman_RLM_FALSE |
0.578 |
0.615 |
0.535 |
0.327 |
0.0765 |
0.00917 |
| T_Driven_spearman_RLM_TRUE |
0.642 |
0.651 |
0.566 |
0.364 |
0.0979 |
0.00917 |
pander::pander(Base_FractionM)
| T_Blind_fast_LM_FALSE |
0.0122 |
0.147 |
0.199 |
0.211 |
0.0703 |
0.01223 |
| T_Blind_fast_LM_TRUE |
0.0734 |
0.110 |
0.147 |
0.180 |
0.0673 |
0.01223 |
| T_Blind_fast_RLM_FALSE |
0.0122 |
0.147 |
0.199 |
0.211 |
0.0703 |
0.01223 |
| T_Blind_fast_RLM_TRUE |
0.0734 |
0.110 |
0.147 |
0.180 |
0.0673 |
0.01223 |
| T_Blind_pearson_LM_FALSE |
0.0153 |
0.147 |
0.199 |
0.211 |
0.0703 |
0.01223 |
| T_Blind_pearson_LM_TRUE |
0.0703 |
0.110 |
0.147 |
0.180 |
0.0673 |
0.01223 |
| T_Blind_pearson_RLM_FALSE |
0.1529 |
0.162 |
0.196 |
0.211 |
0.0703 |
0.01223 |
| T_Blind_pearson_RLM_TRUE |
0.1009 |
0.110 |
0.138 |
0.177 |
0.0673 |
0.01223 |
| T_Blind_spearman_LM_FALSE |
0.1162 |
0.138 |
0.190 |
0.190 |
0.0673 |
0.00917 |
| T_Blind_spearman_LM_TRUE |
0.0734 |
0.135 |
0.141 |
0.131 |
0.0673 |
0.00917 |
| T_Blind_spearman_RLM_FALSE |
0.1590 |
0.180 |
0.183 |
0.187 |
0.0703 |
0.00917 |
| T_Blind_spearman_RLM_TRUE |
0.1131 |
0.131 |
0.147 |
0.138 |
0.0703 |
0.00917 |
| T_Driven_fast_LM_FALSE |
0.1865 |
0.144 |
0.202 |
0.214 |
0.0703 |
0.01223 |
| T_Driven_fast_LM_TRUE |
0.0428 |
0.110 |
0.165 |
0.165 |
0.0642 |
0.01223 |
| T_Driven_fast_RLM_FALSE |
0.1865 |
0.144 |
0.202 |
0.214 |
0.0703 |
0.01223 |
| T_Driven_fast_RLM_TRUE |
0.0428 |
0.110 |
0.165 |
0.165 |
0.0642 |
0.01223 |
| T_Driven_pearson_LM_FALSE |
0.1804 |
0.141 |
0.202 |
0.214 |
0.0703 |
0.01223 |
| T_Driven_pearson_LM_TRUE |
0.0122 |
0.110 |
0.165 |
0.165 |
0.0642 |
0.01223 |
| T_Driven_pearson_RLM_FALSE |
0.1957 |
0.156 |
0.190 |
0.220 |
0.0703 |
0.01223 |
| T_Driven_pearson_RLM_TRUE |
0.1315 |
0.128 |
0.165 |
0.168 |
0.0642 |
0.01223 |
| T_Driven_spearman_LM_FALSE |
0.1682 |
0.177 |
0.187 |
0.190 |
0.0612 |
0.00917 |
| T_Driven_spearman_LM_TRUE |
0.1131 |
0.162 |
0.150 |
0.144 |
0.0612 |
0.00917 |
| T_Driven_spearman_RLM_FALSE |
0.1682 |
0.165 |
0.187 |
0.193 |
0.0642 |
0.00917 |
| T_Driven_spearman_RLM_TRUE |
0.1315 |
0.144 |
0.156 |
0.141 |
0.0642 |
0.00917 |
pander::pander(Unaltered_FractionM)
| T_Blind_fast_LM_FALSE |
0.0214 |
0.263 |
0.450 |
0.621 |
0.908 |
0.988 |
| T_Blind_fast_LM_TRUE |
0.2477 |
0.220 |
0.398 |
0.602 |
0.899 |
0.988 |
| T_Blind_fast_RLM_FALSE |
0.0214 |
0.263 |
0.450 |
0.621 |
0.908 |
0.988 |
| T_Blind_fast_RLM_TRUE |
0.2477 |
0.220 |
0.398 |
0.602 |
0.899 |
0.988 |
| T_Blind_pearson_LM_FALSE |
0.0245 |
0.263 |
0.450 |
0.621 |
0.908 |
0.988 |
| T_Blind_pearson_LM_TRUE |
0.2263 |
0.220 |
0.398 |
0.602 |
0.899 |
0.988 |
| T_Blind_pearson_RLM_FALSE |
0.3394 |
0.355 |
0.453 |
0.621 |
0.908 |
0.988 |
| T_Blind_pearson_RLM_TRUE |
0.3089 |
0.300 |
0.388 |
0.602 |
0.899 |
0.988 |
| T_Blind_spearman_LM_FALSE |
0.3609 |
0.330 |
0.477 |
0.654 |
0.924 |
0.991 |
| T_Blind_spearman_LM_TRUE |
0.2936 |
0.300 |
0.422 |
0.627 |
0.902 |
0.991 |
| T_Blind_spearman_RLM_FALSE |
0.4159 |
0.398 |
0.465 |
0.654 |
0.924 |
0.991 |
| T_Blind_spearman_RLM_TRUE |
0.3425 |
0.330 |
0.428 |
0.633 |
0.902 |
0.991 |
| T_Driven_fast_LM_FALSE |
0.4006 |
0.266 |
0.459 |
0.636 |
0.902 |
0.988 |
| T_Driven_fast_LM_TRUE |
0.0887 |
0.220 |
0.416 |
0.612 |
0.899 |
0.988 |
| T_Driven_fast_RLM_FALSE |
0.4006 |
0.266 |
0.459 |
0.636 |
0.902 |
0.988 |
| T_Driven_fast_RLM_TRUE |
0.0887 |
0.220 |
0.416 |
0.612 |
0.899 |
0.988 |
| T_Driven_pearson_LM_FALSE |
0.3945 |
0.263 |
0.459 |
0.636 |
0.902 |
0.988 |
| T_Driven_pearson_LM_TRUE |
0.0183 |
0.220 |
0.416 |
0.612 |
0.899 |
0.988 |
| T_Driven_pearson_RLM_FALSE |
0.4251 |
0.346 |
0.440 |
0.645 |
0.902 |
0.988 |
| T_Driven_pearson_RLM_TRUE |
0.3547 |
0.324 |
0.413 |
0.612 |
0.899 |
0.988 |
| T_Driven_spearman_LM_FALSE |
0.4557 |
0.394 |
0.468 |
0.673 |
0.924 |
0.991 |
| T_Driven_spearman_LM_TRUE |
0.3914 |
0.355 |
0.440 |
0.636 |
0.902 |
0.991 |
| T_Driven_spearman_RLM_FALSE |
0.4220 |
0.385 |
0.465 |
0.673 |
0.924 |
0.991 |
| T_Driven_spearman_RLM_TRUE |
0.3578 |
0.349 |
0.434 |
0.636 |
0.902 |
0.991 |
pander::pander(sparcityM)
| T_Blind_fast_LM_FALSE |
0.34368 |
0.04076 |
0.00666 |
0.00465 |
0.00338 |
0.00310 |
| T_Blind_fast_LM_TRUE |
0.01914 |
0.06419 |
0.00795 |
0.00483 |
0.00340 |
0.00310 |
| T_Blind_fast_RLM_FALSE |
0.34368 |
0.04076 |
0.00666 |
0.00465 |
0.00338 |
0.00310 |
| T_Blind_fast_RLM_TRUE |
0.01914 |
0.06419 |
0.00795 |
0.00483 |
0.00340 |
0.00310 |
| T_Blind_pearson_LM_FALSE |
0.36673 |
0.04076 |
0.00666 |
0.00465 |
0.00338 |
0.00310 |
| T_Blind_pearson_LM_TRUE |
0.02012 |
0.06419 |
0.00795 |
0.00483 |
0.00340 |
0.00310 |
| T_Blind_pearson_RLM_FALSE |
0.01009 |
0.01056 |
0.00706 |
0.00468 |
0.00338 |
0.00310 |
| T_Blind_pearson_RLM_TRUE |
0.00899 |
0.01011 |
0.00837 |
0.00483 |
0.00340 |
0.00310 |
| T_Blind_spearman_LM_FALSE |
0.00790 |
0.03341 |
0.00696 |
0.00450 |
0.00334 |
0.00309 |
| T_Blind_spearman_LM_TRUE |
0.01390 |
0.03865 |
0.00835 |
0.00462 |
0.00341 |
0.00309 |
| T_Blind_spearman_RLM_FALSE |
0.00775 |
0.00867 |
0.00738 |
0.00450 |
0.00335 |
0.00309 |
| T_Blind_spearman_RLM_TRUE |
0.01076 |
0.01001 |
0.00772 |
0.00467 |
0.00342 |
0.00309 |
| T_Driven_fast_LM_FALSE |
0.00798 |
0.04196 |
0.00689 |
0.00454 |
0.00339 |
0.00310 |
| T_Driven_fast_LM_TRUE |
0.07902 |
0.06023 |
0.00778 |
0.00452 |
0.00339 |
0.00310 |
| T_Driven_fast_RLM_FALSE |
0.00798 |
0.04196 |
0.00689 |
0.00454 |
0.00339 |
0.00310 |
| T_Driven_fast_RLM_TRUE |
0.07902 |
0.06023 |
0.00778 |
0.00452 |
0.00339 |
0.00310 |
| T_Driven_pearson_LM_FALSE |
0.00822 |
0.04234 |
0.00689 |
0.00454 |
0.00339 |
0.00310 |
| T_Driven_pearson_LM_TRUE |
0.19312 |
0.06023 |
0.00778 |
0.00452 |
0.00339 |
0.00310 |
| T_Driven_pearson_RLM_FALSE |
0.00722 |
0.00916 |
0.00714 |
0.00450 |
0.00339 |
0.00310 |
| T_Driven_pearson_RLM_TRUE |
0.00835 |
0.01034 |
0.00774 |
0.00456 |
0.00339 |
0.00310 |
| T_Driven_spearman_LM_FALSE |
0.00659 |
0.00772 |
0.00633 |
0.00437 |
0.00331 |
0.00309 |
| T_Driven_spearman_LM_TRUE |
0.00823 |
0.00852 |
0.00812 |
0.00446 |
0.00339 |
0.00309 |
| T_Driven_spearman_RLM_FALSE |
0.00762 |
0.00850 |
0.00685 |
0.00435 |
0.00332 |
0.00309 |
| T_Driven_spearman_RLM_TRUE |
0.00926 |
0.00860 |
0.00777 |
0.00445 |
0.00339 |
0.00309 |
pander::pander(Average_Latent_SizeM)
| T_Blind_fast_LM_FALSE |
121.85 |
19.74 |
2.84 |
2.33 |
2.09 |
2 |
| T_Blind_fast_LM_TRUE |
6.78 |
29.04 |
3.39 |
2.55 |
2.09 |
2 |
| T_Blind_fast_RLM_FALSE |
121.85 |
19.74 |
2.84 |
2.33 |
2.09 |
2 |
| T_Blind_fast_RLM_TRUE |
6.78 |
29.04 |
3.39 |
2.55 |
2.09 |
2 |
| T_Blind_pearson_LM_FALSE |
115.17 |
19.74 |
2.84 |
2.33 |
2.09 |
2 |
| T_Blind_pearson_LM_TRUE |
6.83 |
29.04 |
3.39 |
2.55 |
2.09 |
2 |
| T_Blind_pearson_RLM_FALSE |
5.07 |
5.94 |
3.72 |
2.36 |
2.10 |
2 |
| T_Blind_pearson_RLM_TRUE |
3.31 |
4.45 |
3.94 |
2.55 |
2.08 |
2 |
| T_Blind_spearman_LM_FALSE |
3.52 |
13.94 |
4.16 |
2.39 |
2.18 |
2 |
| T_Blind_spearman_LM_TRUE |
6.19 |
14.27 |
3.85 |
2.39 |
2.25 |
2 |
| T_Blind_spearman_RLM_FALSE |
3.98 |
4.77 |
3.71 |
2.41 |
2.30 |
2 |
| T_Blind_spearman_RLM_TRUE |
4.09 |
3.57 |
3.76 |
2.45 |
2.31 |
2 |
| T_Driven_fast_LM_FALSE |
4.77 |
18.68 |
3.76 |
2.34 |
2.09 |
2 |
| T_Driven_fast_LM_TRUE |
22.22 |
29.11 |
4.09 |
2.14 |
2.09 |
2 |
| T_Driven_fast_RLM_FALSE |
4.77 |
18.68 |
3.76 |
2.34 |
2.09 |
2 |
| T_Driven_fast_RLM_TRUE |
22.22 |
29.11 |
4.09 |
2.14 |
2.09 |
2 |
| T_Driven_pearson_LM_FALSE |
5.39 |
19.91 |
3.76 |
2.34 |
2.09 |
2 |
| T_Driven_pearson_LM_TRUE |
67.38 |
29.11 |
4.09 |
2.14 |
2.09 |
2 |
| T_Driven_pearson_RLM_FALSE |
3.55 |
4.65 |
3.62 |
2.42 |
2.09 |
2 |
| T_Driven_pearson_RLM_TRUE |
3.78 |
5.17 |
3.67 |
2.14 |
2.08 |
2 |
| T_Driven_spearman_LM_FALSE |
3.00 |
4.58 |
3.21 |
2.33 |
2.12 |
NA |
| T_Driven_spearman_LM_TRUE |
3.72 |
3.69 |
3.62 |
2.27 |
2.17 |
NA |
| T_Driven_spearman_RLM_FALSE |
4.50 |
5.25 |
3.63 |
2.38 |
2.25 |
2 |
| T_Driven_spearman_RLM_TRUE |
3.83 |
3.74 |
3.37 |
2.30 |
2.23 |
2 |
pander::pander(SigDeM)
| T_Blind_fast_LM_FALSE |
6 |
7 |
9 |
11 |
2 |
1 |
| T_Blind_fast_LM_TRUE |
6 |
6 |
7 |
18 |
7 |
1 |
| T_Blind_fast_RLM_FALSE |
6 |
7 |
9 |
11 |
2 |
1 |
| T_Blind_fast_RLM_TRUE |
6 |
6 |
7 |
18 |
7 |
1 |
| T_Blind_pearson_LM_FALSE |
6 |
7 |
9 |
11 |
2 |
1 |
| T_Blind_pearson_LM_TRUE |
6 |
6 |
7 |
18 |
7 |
1 |
| T_Blind_pearson_RLM_FALSE |
10 |
10 |
12 |
11 |
2 |
1 |
| T_Blind_pearson_RLM_TRUE |
13 |
9 |
11 |
23 |
7 |
1 |
| T_Blind_spearman_LM_FALSE |
11 |
7 |
12 |
11 |
3 |
0 |
| T_Blind_spearman_LM_TRUE |
8 |
3 |
12 |
24 |
3 |
0 |
| T_Blind_spearman_RLM_FALSE |
10 |
11 |
8 |
11 |
3 |
0 |
| T_Blind_spearman_RLM_TRUE |
10 |
13 |
9 |
24 |
3 |
0 |
| T_Driven_fast_LM_FALSE |
2 |
2 |
8 |
7 |
0 |
1 |
| T_Driven_fast_LM_TRUE |
1 |
2 |
10 |
9 |
5 |
1 |
| T_Driven_fast_RLM_FALSE |
2 |
2 |
8 |
7 |
0 |
1 |
| T_Driven_fast_RLM_TRUE |
1 |
2 |
10 |
9 |
5 |
1 |
| T_Driven_pearson_LM_FALSE |
1 |
2 |
8 |
7 |
0 |
1 |
| T_Driven_pearson_LM_TRUE |
1 |
2 |
10 |
9 |
5 |
1 |
| T_Driven_pearson_RLM_FALSE |
7 |
7 |
10 |
9 |
0 |
0 |
| T_Driven_pearson_RLM_TRUE |
7 |
6 |
11 |
10 |
4 |
0 |
| T_Driven_spearman_LM_FALSE |
2 |
2 |
5 |
7 |
1 |
0 |
| T_Driven_spearman_LM_TRUE |
2 |
1 |
5 |
11 |
1 |
0 |
| T_Driven_spearman_RLM_FALSE |
12 |
7 |
7 |
6 |
0 |
0 |
| T_Driven_spearman_RLM_TRUE |
5 |
5 |
6 |
11 |
1 |
0 |
pander::pander(La_SignificantM)
| T_Blind_fast_LM_FALSE |
20 |
31 |
61 |
90 |
143 |
157 |
| T_Blind_fast_LM_TRUE |
29 |
30 |
61 |
101 |
139 |
157 |
| T_Blind_fast_RLM_FALSE |
20 |
31 |
61 |
90 |
143 |
157 |
| T_Blind_fast_RLM_TRUE |
29 |
30 |
61 |
101 |
139 |
157 |
| T_Blind_pearson_LM_FALSE |
18 |
31 |
61 |
90 |
143 |
157 |
| T_Blind_pearson_LM_TRUE |
30 |
30 |
61 |
101 |
139 |
157 |
| T_Blind_pearson_RLM_FALSE |
71 |
69 |
82 |
88 |
142 |
157 |
| T_Blind_pearson_RLM_TRUE |
47 |
54 |
67 |
101 |
141 |
157 |
| T_Blind_spearman_LM_FALSE |
53 |
45 |
74 |
92 |
143 |
157 |
| T_Blind_spearman_LM_TRUE |
29 |
34 |
59 |
117 |
135 |
157 |
| T_Blind_spearman_RLM_FALSE |
65 |
65 |
65 |
93 |
142 |
157 |
| T_Blind_spearman_RLM_TRUE |
43 |
49 |
71 |
116 |
136 |
157 |
| T_Driven_fast_LM_FALSE |
32 |
32 |
60 |
107 |
142 |
156 |
| T_Driven_fast_LM_TRUE |
10 |
25 |
69 |
104 |
141 |
156 |
| T_Driven_fast_RLM_FALSE |
32 |
32 |
60 |
107 |
142 |
156 |
| T_Driven_fast_RLM_TRUE |
10 |
25 |
69 |
104 |
141 |
156 |
| T_Driven_pearson_LM_FALSE |
26 |
32 |
60 |
107 |
142 |
156 |
| T_Driven_pearson_LM_TRUE |
9 |
25 |
69 |
104 |
141 |
156 |
| T_Driven_pearson_RLM_FALSE |
61 |
59 |
68 |
116 |
142 |
157 |
| T_Driven_pearson_RLM_TRUE |
51 |
52 |
63 |
107 |
142 |
157 |
| T_Driven_spearman_LM_FALSE |
13 |
65 |
65 |
107 |
141 |
156 |
| T_Driven_spearman_LM_TRUE |
26 |
37 |
67 |
113 |
136 |
156 |
| T_Driven_spearman_RLM_FALSE |
57 |
55 |
60 |
113 |
141 |
157 |
| T_Driven_spearman_RLM_TRUE |
39 |
44 |
64 |
119 |
137 |
157 |
pander::pander(pbKNNaucM)
| T_Blind_fast_LM_FALSE |
0.728 |
0.727 |
0.768 |
0.772 |
0.773 |
0.771 |
| T_Blind_fast_LM_TRUE |
0.743 |
0.744 |
0.744 |
0.768 |
0.774 |
0.771 |
| T_Blind_fast_RLM_FALSE |
0.728 |
0.727 |
0.768 |
0.772 |
0.773 |
0.771 |
| T_Blind_fast_RLM_TRUE |
0.743 |
0.744 |
0.744 |
0.768 |
0.774 |
0.771 |
| T_Blind_pearson_LM_FALSE |
0.751 |
0.727 |
0.768 |
0.772 |
0.773 |
0.771 |
| T_Blind_pearson_LM_TRUE |
0.744 |
0.744 |
0.744 |
0.768 |
0.774 |
0.771 |
| T_Blind_pearson_RLM_FALSE |
0.747 |
0.753 |
0.776 |
0.768 |
0.774 |
0.769 |
| T_Blind_pearson_RLM_TRUE |
0.737 |
0.743 |
0.736 |
0.772 |
0.763 |
0.769 |
| T_Blind_spearman_LM_FALSE |
0.765 |
0.775 |
0.771 |
0.773 |
0.783 |
0.772 |
| T_Blind_spearman_LM_TRUE |
0.745 |
0.755 |
0.774 |
0.756 |
0.783 |
0.772 |
| T_Blind_spearman_RLM_FALSE |
0.743 |
0.754 |
0.779 |
0.749 |
0.788 |
0.774 |
| T_Blind_spearman_RLM_TRUE |
0.783 |
0.759 |
0.780 |
0.766 |
0.774 |
0.774 |
| T_Driven_fast_LM_FALSE |
0.754 |
0.725 |
0.757 |
0.772 |
0.786 |
0.764 |
| T_Driven_fast_LM_TRUE |
0.707 |
0.723 |
0.731 |
0.779 |
0.769 |
0.764 |
| T_Driven_fast_RLM_FALSE |
0.754 |
0.725 |
0.757 |
0.772 |
0.786 |
0.764 |
| T_Driven_fast_RLM_TRUE |
0.707 |
0.723 |
0.731 |
0.779 |
0.769 |
0.764 |
| T_Driven_pearson_LM_FALSE |
0.777 |
0.723 |
0.757 |
0.772 |
0.786 |
0.764 |
| T_Driven_pearson_LM_TRUE |
0.715 |
0.723 |
0.731 |
0.779 |
0.769 |
0.764 |
| T_Driven_pearson_RLM_FALSE |
0.785 |
0.765 |
0.770 |
0.759 |
0.781 |
0.769 |
| T_Driven_pearson_RLM_TRUE |
0.741 |
0.750 |
0.776 |
0.758 |
0.768 |
0.769 |
| T_Driven_spearman_LM_FALSE |
0.764 |
0.740 |
0.766 |
0.779 |
0.782 |
0.771 |
| T_Driven_spearman_LM_TRUE |
0.747 |
0.766 |
0.747 |
0.764 |
0.780 |
0.771 |
| T_Driven_spearman_RLM_FALSE |
0.759 |
0.738 |
0.783 |
0.770 |
0.783 |
0.773 |
| T_Driven_spearman_RLM_TRUE |
0.756 |
0.784 |
0.758 |
0.761 |
0.769 |
0.773 |
pander::pander(pbKNNaccM)
| T_Blind_fast_LM_FALSE |
0.674 |
0.706 |
0.715 |
0.694 |
0.697 |
0.685 |
| T_Blind_fast_LM_TRUE |
0.7 |
0.709 |
0.682 |
0.7 |
0.709 |
0.685 |
| T_Blind_fast_RLM_FALSE |
0.674 |
0.706 |
0.715 |
0.694 |
0.697 |
0.685 |
| T_Blind_fast_RLM_TRUE |
0.7 |
0.709 |
0.682 |
0.7 |
0.709 |
0.685 |
| T_Blind_pearson_LM_FALSE |
0.697 |
0.706 |
0.715 |
0.694 |
0.697 |
0.685 |
| T_Blind_pearson_LM_TRUE |
0.7 |
0.709 |
0.682 |
0.7 |
0.709 |
0.685 |
| T_Blind_pearson_RLM_FALSE |
0.709 |
0.712 |
0.718 |
0.694 |
0.694 |
0.691 |
| T_Blind_pearson_RLM_TRUE |
0.688 |
0.7 |
0.697 |
0.715 |
0.697 |
0.691 |
| T_Blind_spearman_LM_FALSE |
0.7 |
0.721 |
0.703 |
0.724 |
0.724 |
0.694 |
| T_Blind_spearman_LM_TRUE |
0.706 |
0.721 |
0.712 |
0.694 |
0.724 |
0.694 |
| T_Blind_spearman_RLM_FALSE |
0.697 |
0.671 |
0.724 |
0.697 |
0.721 |
0.697 |
| T_Blind_spearman_RLM_TRUE |
0.724 |
0.691 |
0.718 |
0.709 |
0.726 |
0.697 |
| T_Driven_fast_LM_FALSE |
0.691 |
0.682 |
0.703 |
0.703 |
0.712 |
0.697 |
| T_Driven_fast_LM_TRUE |
0.718 |
0.718 |
0.688 |
0.706 |
0.709 |
0.697 |
| T_Driven_fast_RLM_FALSE |
0.691 |
0.682 |
0.703 |
0.703 |
0.712 |
0.697 |
| T_Driven_fast_RLM_TRUE |
0.718 |
0.718 |
0.688 |
0.706 |
0.709 |
0.697 |
| T_Driven_pearson_LM_FALSE |
0.694 |
0.668 |
0.703 |
0.703 |
0.712 |
0.697 |
| T_Driven_pearson_LM_TRUE |
0.694 |
0.718 |
0.688 |
0.706 |
0.709 |
0.697 |
| T_Driven_pearson_RLM_FALSE |
0.691 |
0.7 |
0.712 |
0.7 |
0.7 |
0.688 |
| T_Driven_pearson_RLM_TRUE |
0.706 |
0.712 |
0.718 |
0.703 |
0.7 |
0.688 |
| T_Driven_spearman_LM_FALSE |
0.703 |
0.703 |
0.703 |
0.718 |
0.715 |
0.706 |
| T_Driven_spearman_LM_TRUE |
0.7 |
0.712 |
0.694 |
0.703 |
0.729 |
0.706 |
| T_Driven_spearman_RLM_FALSE |
0.668 |
0.709 |
0.724 |
0.682 |
0.709 |
0.697 |
| T_Driven_spearman_RLM_TRUE |
0.691 |
0.676 |
0.7 |
0.697 |
0.724 |
0.697 |
miny = min(pbKNNaucM)-0.05
maxy = max(pbKNNaucM)+0.15
plot(thr,pbKNNaucM[1,],ylim=c(miny,maxy),
main="KNN's ROCAUC",
xlab="Correlation-Matrix's Maximum Goal",
ylab="ROC AUC",
type="l",
col=1,
lwd=2)
for (ind in 2:nrow(pbKNNaucM))
{
lines(thr,pbKNNaucM[ind,],col=ind,lwd=2,lty=ind)
}
legend("topleft", rownames(pbKNNaucM),lty=1:length(thenames), col = 1:length(thenames),cex=0.55,ncol=2)
fastRows <- str_detect(rownames(pbKNNaucM),"fast")
pearsonRows <- str_detect(rownames(pbKNNaucM),"pearson")
spearmanRows <- str_detect(rownames(pbKNNaucM),"spearman")
T_BlindRows <- str_detect(rownames(pbKNNaucM),"T_Blind")
corRankRows <- str_detect(rownames(pbKNNaucM),"TRUE")
maxCorRankRows <- str_detect(rownames(pbKNNaucM),"FALSE")
RLMfitMethod <- str_detect(rownames(pbKNNaucM),"RLM")
meanAuc <- colMeans(pbKNNaucM[fastRows,])
meanAuc <- rbind(meanAuc,colMeans(pbKNNaucM[pearsonRows,]))
meanAuc <- rbind(meanAuc,colMeans(pbKNNaucM[spearmanRows,]))
meanAuc <- rbind(meanAuc,colMeans(pbKNNaucM[!T_BlindRows,]))
meanAuc <- rbind(meanAuc,colMeans(pbKNNaucM[T_BlindRows,]))
meanAuc <- rbind(meanAuc,colMeans(pbKNNaucM[corRankRows,]))
meanAuc <- rbind(meanAuc,colMeans(pbKNNaucM[maxCorRankRows,]))
meanAuc <- rbind(meanAuc,colMeans(pbKNNaucM[RLMfitMethod,]))
meanAuc <- rbind(meanAuc,colMeans(pbKNNaucM[!RLMfitMethod,]))
legnames <- c("fast","Pearson","Spearman","T_Driven","T_Blind","SumCor","MaxCor","RLM","LM")
pbKNNaccM <- as.data.frame(pbKNNaccM)
pbKNNaccM[,1:ncol(pbKNNaccM)] <- sapply(pbKNNaccM,as.numeric)
Average_Latent_SizeM <- as.data.frame(Average_Latent_SizeM)
Average_Latent_SizeM[,1:ncol(Average_Latent_SizeM)] <- sapply(Average_Latent_SizeM,as.numeric)
Average_Latent_SizeM[is.na(Average_Latent_SizeM)] <- 0
SigDeM <- as.data.frame(SigDeM)
SigDeM[,1:ncol(SigDeM)] <- sapply(SigDeM,as.numeric)
sparcityM <- as.data.frame(sparcityM)
sparcityM[,1:ncol(sparcityM)] <- sapply(sparcityM,as.numeric)
miny = min(meanAuc)-0.01
maxy = max(meanAuc)+0.025
plot(thr,meanAuc[1,],ylim=c(miny,maxy),
main="Mean KNN's ROCAUC",
xlab="Correlation-Matrix's Maximum Goal",
ylab="ROC AUC",
type="l",
col=1,
lwd=2,
lty=1)
for (ind in 2:nrow(meanAuc))
{
lines(thr,meanAuc[ind,],col=ind,lwd=2,lty=ind)
}
legend("topleft", legnames,lty=1:length(legnames), col = 1:length(legnames),cex=0.75)

miny = min(pbKNNaccM) - 0.025
maxy = max(pbKNNaccM) + 0.1
plot(thr,pbKNNaccM[1,],ylim=c(miny,maxy),
main="KNN's Accuracy",
xlab="Correlation-Matrix's Maximum Goal",
ylab="Accuracy",
type="l",
col=1,
lwd=2)
for (ind in 2:nrow(pbKNNaucM))
{
lines(thr,pbKNNaccM[ind,],col=ind,lwd=2,lty=ind)
}
legend("topleft", rownames(pbKNNaucM),lty=1:length(thenames), col = 1:length(thenames),cex=0.55,ncol=2)
meanAcc <- colMeans(pbKNNaccM[fastRows,])
meanAcc <- rbind(meanAcc,colMeans(pbKNNaccM[pearsonRows,]))
meanAcc <- rbind(meanAcc,colMeans(pbKNNaccM[spearmanRows,]))
meanAcc <- rbind(meanAcc,colMeans(pbKNNaccM[!T_BlindRows,]))
meanAcc <- rbind(meanAcc,colMeans(pbKNNaccM[T_BlindRows,]))
meanAcc <- rbind(meanAcc,colMeans(pbKNNaccM[corRankRows,]))
meanAcc <- rbind(meanAcc,colMeans(pbKNNaccM[maxCorRankRows,]))
meanAcc <- rbind(meanAcc,colMeans(pbKNNaccM[RLMfitMethod,]))
meanAcc <- rbind(meanAcc,colMeans(pbKNNaccM[!RLMfitMethod,]))
miny = min(meanAcc)-0.01
maxy = max(meanAcc)+0.025
plot(thr,meanAcc[1,],ylim=c(miny,maxy),
main="Mean KNN's Accuracy",
xlab="Correlation-Matrix's Maximum Goal",
ylab="Accuracy",
type="l",
col=1,
lwd=2)
for (ind in 2:nrow(meanAcc))
{
lines(thr,meanAcc[ind,],col=ind,lwd=2,lty=ind)
}
legend("topleft", legnames,lty=1:length(legnames), col = 1:length(legnames),cex=0.75)

miny = 1
maxy = max(Average_Latent_SizeM)
plot(thr,Average_Latent_SizeM[1,],ylim=c(miny,maxy),
main="Average Size of Latent-Variable",
xlab="Correlation-Matrix's Maximum Goal",
ylab="Size",
type="l",
col=1,
lwd=2,
log = "y")
for (ind in 2:nrow(Average_Latent_SizeM))
{
lines(thr,Average_Latent_SizeM[ind,],col=ind,lwd=2,lty=ind)
}
legend("topright", rownames(Average_Latent_SizeM),lty=1:length(thenames), col = 1:length(thenames),cex=0.55,ncol=2)
meanAccAvgSize <- colMeans(Average_Latent_SizeM[fastRows,])
meanAccAvgSize <- rbind(meanAccAvgSize,colMeans(Average_Latent_SizeM[pearsonRows,]))
meanAccAvgSize <- rbind(meanAccAvgSize,colMeans(Average_Latent_SizeM[spearmanRows,]))
meanAccAvgSize <- rbind(meanAccAvgSize,colMeans(Average_Latent_SizeM[!T_BlindRows,]))
meanAccAvgSize <- rbind(meanAccAvgSize,colMeans(Average_Latent_SizeM[T_BlindRows,]))
meanAccAvgSize <- rbind(meanAccAvgSize,colMeans(Average_Latent_SizeM[corRankRows,]))
meanAccAvgSize <- rbind(meanAccAvgSize,colMeans(Average_Latent_SizeM[maxCorRankRows,]))
meanAccAvgSize <- rbind(meanAccAvgSize,colMeans(Average_Latent_SizeM[RLMfitMethod,]))
meanAccAvgSize <- rbind(meanAccAvgSize,colMeans(Average_Latent_SizeM[!RLMfitMethod,]))
miny =1
maxy = max(meanAccAvgSize) + 10
plot(thr,meanAccAvgSize[1,],ylim=c(miny,maxy),
main="Mean Size of Average-Latent-Variable",
xlab="Correlation-Matrix's Maximum Goal",
ylab="Size",
type="l",
col=1,
lwd=2,
log = "y")
for (ind in 2:nrow(meanAccAvgSize))
{
lines(thr,meanAccAvgSize[ind,],col=ind,lwd=2,lty=ind)
}
legend("topright", legnames,lty=1:length(legnames), col = 1:length(legnames),cex=0.75)

miny = min(La_SignificantM)
maxy = max(La_SignificantM)
plot(thr,La_SignificantM[1,],ylim=c(miny,maxy),
main="Number of Discovered Features",
xlab="Correlation-Matrix's Maximum Goal",
ylab="Number of Features",
type="l",
col=1,
lwd=2,
log = "y")
for (ind in 2:nrow(La_SignificantM))
{
lines(thr,La_SignificantM[ind,],col=ind,lwd=2,lty=ind)
}
legend("bottomright", rownames(La_SignificantM),lty=1:length(thenames), col = 1:length(thenames),cex=0.55,ncol=2)
meanDiscovered <- colMeans(La_SignificantM[fastRows,])
meanDiscovered <- rbind(meanDiscovered,colMeans(La_SignificantM[pearsonRows,]))
meanDiscovered <- rbind(meanDiscovered,colMeans(La_SignificantM[spearmanRows,]))
meanDiscovered <- rbind(meanDiscovered,colMeans(La_SignificantM[!T_BlindRows,]))
meanDiscovered <- rbind(meanDiscovered,colMeans(La_SignificantM[T_BlindRows,]))
meanDiscovered <- rbind(meanDiscovered,colMeans(La_SignificantM[corRankRows,]))
meanDiscovered <- rbind(meanDiscovered,colMeans(La_SignificantM[maxCorRankRows,]))
meanDiscovered <- rbind(meanDiscovered,colMeans(La_SignificantM[RLMfitMethod,]))
meanDiscovered <- rbind(meanDiscovered,colMeans(La_SignificantM[!RLMfitMethod,]))
miny = min(meanDiscovered)
maxy = max(meanDiscovered) + 10
plot(thr,meanDiscovered[1,],ylim=c(miny,maxy),
main="Average Number of Discovered Features",
xlab="Correlation-Matrix's Maximum Goal",
ylab="Number of Features",
type="l",
col=1,
lwd=2,
log = "y")
for (ind in 2:nrow(meanDiscovered))
{
lines(thr,meanDiscovered[ind,],col=ind,lwd=2,lty=ind)
}
legend("bottomright", legnames,lty=1:length(legnames), col = 1:length(legnames),cex=0.75)

SigDeM[is.na(SigDeM)] <- 0
miny = 1
maxy = max(SigDeM) + 200
plot(thr,SigDeM[1,],ylim=c(miny,maxy),
main="Number of Significant Latent Variables",
xlab="Correlation-Matrix's Maximum Goal",
ylab="How Many",
type="l",
col=1,
lwd=2,
log = "y")
for (ind in 2:nrow(SigDeM))
{
lines(thr,SigDeM[ind,],col=ind,lwd=2,lty=ind)
}
legend("topright", rownames(SigDeM),lty=1:length(thenames), col = 1:length(thenames),cex=0.55,ncol=2)
SigLatent <- colMeans(SigDeM[fastRows,])
SigLatent <- rbind(SigLatent,colMeans(SigDeM[pearsonRows,]))
SigLatent <- rbind(SigLatent,colMeans(SigDeM[spearmanRows,]))
SigLatent <- rbind(SigLatent,colMeans(SigDeM[!T_BlindRows,]))
SigLatent <- rbind(SigLatent,colMeans(SigDeM[T_BlindRows,]))
SigLatent <- rbind(SigLatent,colMeans(SigDeM[corRankRows,]))
SigLatent <- rbind(SigLatent,colMeans(SigDeM[maxCorRankRows,]))
SigLatent <- rbind(SigLatent,colMeans(SigDeM[RLMfitMethod,]))
SigLatent <- rbind(SigLatent,colMeans(SigDeM[!RLMfitMethod,]))
miny = 1
maxy = max(SigLatent) + 10
plot(thr,SigLatent[1,],ylim=c(miny,maxy),
main="Average # of Significant Latent Variables",
xlab="Correlation-Matrix's Maximum Goal",
ylab="How Many",
type="l",
col=1,
lwd=2,
log = "y")
for (ind in 2:nrow(SigLatent))
{
lines(thr,SigLatent[ind,],col=ind,lwd=2,lty=ind)
}
legend("topright", legnames,lty=1:length(legnames), col = 1:length(legnames),cex=0.75)

sparcityM[is.na(sparcityM)] <- 0
miny = min(sparcityM)
maxy = max(sparcityM) + 0.25
plot(thr,sparcityM[1,],ylim=c(miny,maxy),
main="Matrix Sparcity",
xlab="Correlation-Matrix's Maximum Goal",
ylab="Sparcity",
type="l",
col=1,
lwd=2,
log = "y")
for (ind in 2:nrow(sparcityM))
{
lines(thr,sparcityM[ind,],col=ind,lwd=2,lty=ind)
}
legend("topright", rownames(sparcityM),lty=1:length(thenames), col = 1:length(thenames),cex=0.55,ncol=2)
meanSparcity <- colMeans(sparcityM[fastRows,])
meanSparcity <- rbind(meanSparcity,colMeans(sparcityM[pearsonRows,]))
meanSparcity <- rbind(meanSparcity,colMeans(sparcityM[spearmanRows,]))
meanSparcity <- rbind(meanSparcity,colMeans(sparcityM[!T_BlindRows,]))
meanSparcity <- rbind(meanSparcity,colMeans(sparcityM[T_BlindRows,]))
meanSparcity <- rbind(meanSparcity,colMeans(sparcityM[corRankRows,]))
meanSparcity <- rbind(meanSparcity,colMeans(sparcityM[maxCorRankRows,]))
meanSparcity <- rbind(meanSparcity,colMeans(sparcityM[RLMfitMethod,]))
meanSparcity <- rbind(meanSparcity,colMeans(sparcityM[!RLMfitMethod,]))
miny = min(meanSparcity)
maxy = max(meanSparcity)+0.25
plot(thr,meanSparcity[1,],ylim=c(miny,maxy),
main="Mean Matrix Sparcity",
xlab="Correlation-Matrix's Maximum Goal",
ylab="Sparcity",
type="l",
col=1,
lwd=2,
log = "y")
for (ind in 2:nrow(meanSparcity))
{
lines(thr,meanSparcity[ind,],col=ind,lwd=2,lty=ind)
}
legend("topright", legnames,lty=1:length(legnames), col = 1:length(legnames),cex=0.75)
